A model for presenting accelerometer paradata in large studies: ISCOLE

Catrine E. Tudor- Locke, Emily F. Mire, Kara N. Dentro, Tiago V. Barreira, John M. Schuna, Pei Zhao, Mark S. Tremblay, Martyn Standage, Olga Sarmiento, Vincent Ochieng Onywera, Tim Olds, Victor Rodrigues Matsudo, José António Maia, Carol Ann Maher, Estelle Victoria Lambert, Anura Vishwanath Kurpad, Rebecca Kuriyan, Gang Hu, Mikael Fogelholm, Jean Philippe ChaputTimothy S. Church, Peter T. Katzmarzyk, Stephanie T. Broyles, Ben P. Butitta, Catherine M. Champagne, Shannon H. Cocreham, Katy Drazba, Deirdre Deirdre M. Harrington, William D. Johnson, Dione Milauskas, Allison Tohme, Ruben Q. Rodarte, Bobby Amoroso, John Luopa, Rebecca H. Neiberg, Scott Rushing, Lucy Kate Lewis, Katia Ferrar, Effie Georgiadis, Rebecca Megan Stanley, Victor Keihan Matsudo, Sandra Mahecha Matsudo, Timóteo Leandro Araújo, Luís Carlos De Oliveira, Leandro Rezende, Luis Fabiano, Diogo Bezerra, Gerson Luis Ferrari, Priscilla Bélanger, Michael Marc Borghese, Charles Boyer, Allana G.W. LeBlanc, Claire E. Francis, Geneviève Leduc, Chengming Diao, Wei Li, Enquing qing Liu, Gongshu Liu, Hongyan Liu, Jian Ma, Yijuan Qiao, Huiguang Tian, Yue Wang, Tao Zhang, Fuxia Zhang, Olga Sarmiento, Julio Acosta, Yalta Alvira, María Paula Díaz, Rocio Gámez, Maria Paula Garcia, Luis Guillermo Gómez, Lisseth Heras González, Silvia Alejandra González, Carlos Grijalba, Leidys Gutiérrez, David Leal, Nicolás Lemus, Etelvina Mahecha, Maria Paula Mahecha, Rosalba Mahecha, Andrea Ramírez Varela, Paola Ríos, Andres Suarez, Camilo A. Triana, Elli Hovi, Jemina Kivelä, Sari M. Räsänen, Sanna Roito, Taru Saloheimo, Leena Valta, Deepa P. Lokesh, Michelle Stephanie D'Almeida, R. Annie Mattilda, Lygia F.M. Correa, D. Vijay, Lucy Joy Wachira, Stella Kagwiria Muthuri, Alessandra Da Silva Borges, Sofia Oliveira Sá Cachada, Raquel Nichele De Chaves, Thayse Natacha Gomes, Sara Isabel Pereira, Daniel Monteiro De Vilhena E Santos, Fernanda Karina Dos Santos, Pedro Gil Da Silva, Michele Caroline De Souza, Vicki E. Lambert, Matthew April, Monika Uys, Nirmala Naidoo, Nandi Synyanya, Madelaine T. Carstens, Sean P. Cumming, Clemens Drenowatz, Lydia G. Emm, Fiona Bridget Gillison, Julia Kirstey Zakrzewski, Ashley Braud, Sheletta G. Donatto, Corbin Lemon, Ana Jackson, Ashunti Pearson, Gina Pennington, Daniel Ragus, Ryan C. Roubion, John M. Schuna, Derek Wiltz, Alan Mark Batterham, Jacqueline Kerr, Michael W. Pratt, Angelo Pietrobelli

Research output: Contribution to journalArticle

9 Citations (Scopus)

Abstract

Background: We present a model for reporting accelerometer paradata (process-related data produced from survey administration) collected in the International Study of Childhood Obesity Lifestyle and the Environment (ISCOLE), a multi-national investigation of >7000 children (averaging 10.5 years of age) sampled from 12 different developed and developing countries and five continents. Methods: ISCOLE employed a 24-hr waist worn 7-day protocol using the ActiGraph GT3X+. Checklists, flow charts, and systematic data queries documented accelerometer paradata from enrollment to data collection and treatment. Paradata included counts of consented and eligible participants, accelerometers distributed for initial and additional monitoring (site specific decisions in the face of initial monitoring failure), inadequate data (e.g., lost/malfunction, insufficient wear time), and averages for waking wear time, valid days of data, participants with valid data (>4 valid days of data, including 1 weekend day), and minutes with implausibly high values (>20,000 activity counts/min). Results: Of 7806 consented participants, 7372 were deemed eligible to participate, 7314 accelerometers were distributed for initial monitoring and another 106 for additional monitoring. 414 accelerometer data files were inadequate (primarily due to insufficient wear time). Only 29 accelerometers were lost during the implementation of ISCOLE worldwide. The final locked data file consisted of 6553 participant files (90.0% relative to number of participants who completed monitoring) with valid waking wear time, averaging 6.5 valid days and 888.4 minutes/day (14.8 hours). We documented 4762 minutes with implausibly high activity count values from 695 unique participants (9.4% of eligible participants and <0.01% of all minutes). Conclusions: Detailed accelerometer paradata is useful for standardizing communication, facilitating study management, improving the representative qualities of surveys, tracking study endpoint attainment, comparing studies, and ultimately anticipating and controlling costs.

Original languageEnglish
Article number52
JournalInternational Journal of Behavioral Nutrition and Physical Activity
Volume12
Issue number1
DOIs
Publication statusPublished - 20 Apr 2015

Keywords

  • Data collection
  • Evaluation studies
  • Exercise
  • Methods
  • Motor activity

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    Tudor- Locke, C. E., Mire, E. F., Dentro, K. N., Barreira, T. V., Schuna, J. M., Zhao, P., Tremblay, M. S., Standage, M., Sarmiento, O., Onywera, V. O., Olds, T., Matsudo, V. R., Maia, J. A., Maher, C. A., Lambert, E. V., Kurpad, A. V., Kuriyan, R., Hu, G., Fogelholm, M., ... Pietrobelli, A. (2015). A model for presenting accelerometer paradata in large studies: ISCOLE. International Journal of Behavioral Nutrition and Physical Activity, 12(1), [52]. https://doi.org/10.1186/s12966-015-0213-5